Maximum likelihood estimation of spatial lag models in the presence of the error-prone variables

• Published in: Communications in statistics. Theory and methods 2023-05, Vol.52 (10), p.3229-3240
• Main Authors: Eralp, Anil, Gokmen, Sahika, Dagalp, Rukiye
• Format: Article
• Language: English
• Subjects: Bias; Data analysis; Econometrics; Error analysis; error-prone variables; Mathematical models; Maximum likelihood estimation; Parameter estimation; simulation study; spatial autoregressive model; Spatial data; Spatial econometrics models; spatial lag model
• ISSN: 0361-0926; 1532-415X; 1532-415X
• DOI: 10.1080/03610926.2022.2147795

The literature has recently devoted close attention to error-prone variables. Nevertheless, only a small number of research have considered measurement error in spatial econometric models. The presence of measurement error in the spatial econometric models needs to be considered as a result of the rise in spatial data analysis, as the relationship between the spatial correlation and measurement error influences parameter estimation. Therefore, in this study, the impacts of classical measurement error on the parameter estimation of the spatial lag model are theoretically examined for both response and explanatory variables. Then, using simulation studies, finite sample properties are investigated for various situations. The major findings indicate that although error-prone response variable has an opposing bias effect on parameter estimations, error-prone explanatory variables have a significant influence effect on the bias of parameter estimations. As a result, it is occasionally possible to obtain unbiased estimates only in certain circumstances.